An Improved Energy Management System for Fuel Cell/ Ultra-capacitor Electric Vehicle Based Fuzzy Logic Control

Hamlat, A.; Sekkour, M.; Mankour, Mohamed; Khalfaoui, Mohamed

doi:10.1007/978-3-030-92038-8_18

Cited by 1 publication

(1 citation statement)

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“…At present, there are two main categories of energy management strategies, the rulebased (RB) energy management strategy [8][9][10] and optimization-based EMS [11][12][13]. Early energy management methods adopted control methods based on clear rules [10,14] and fuzzy control algorithms based on fuzzy rules [15][16][17]. Later, control methods based on global optimization were widely used, such as the dynamic programming (DP) energy management algorithm [18][19][20] and the genetic algorithm (GA) [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Lin,

Wei,

Feng

et al. 2024

Energies

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Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

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